If you understand basic mathematics and know how to program with Python, you're ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they're applied in the real world. In the first chapter alone, you'll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds.
Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material.
You'll explore: Periodic signals and their spectrums; Harmonic structure of simple waveforms; Chirps and other sounds whose spectrum changes over time; Noise signals and natural sources of noise; The autocorrelation function for estimating pitch; The discrete cosine transform (DCT) for compression; The Fast Fourier Transform for spectra ...

Control the performance and stability of the apps you develop in Swift by working with and understanding advanced concepts in data structures and algorithms. All professional developers have to know which data structure and algorithms to use in their development process. Your choice directly affects the performance of your application. With this book, you'll increase the performance of your software, become a better developer, and even pass tricky interview questions better when looking at professional development opportunities.
Guided by compact and practical chapters, you'll learn the nature and proper use of data structures such as arrays, dictionaries, sets, stacks, queues, lists, hash tables, trie, heaps, binary trees, red black trees, and R-trees. Use the main differences among them to determine which will make your applications efficient and faster. Then tackle algorithms. Work with Big O notation; sorting algorithms such as Insertion, Merge, and Quick; Naive and ...

Use this collection of best practices and tips for assessing the health of a solution. This book provides detailed techniques and instructions to quickly diagnose aspects of your Azure cloud solutions.
The initial chapters of this book introduce you to the many facets of Microsoft Azure, explain why and how building for the cloud differs from on-premise development, and outline the need for a comprehensive strategy to debugging and profiling in Azure. You learn the major types of blades (FaaS, SaaS, PaaS, IaaS), how different views can be created for different scenarios, and you will become familiar with the Favorites section, Cost Management & Billing blade, support, and Cloud Shell. You also will know how to leverage Application Insights for application performance management, in order to achieve a seamless cloud development experience. Application Insights, Log Analytics, and database storage topics are covered. The authors further guide you on identity security with ...

As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold. This book of Python projects in machine learning tries to do just that: to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning to help ensure that it is serving us all.
This book will set you up with a Python programming environment if you don't have one already, then provide you with a conceptual understanding of machine learning in the chapter "An Introduction to Machine Learning." What follows next are three Python machine learning projects. They will help you create a machine learning classifier, build a neural network to recognize handwritten digits, and give you a background in deep reinforcement learning through building a bot for Atari. ...

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. ...

PostgreSQL for Beginners book is intended for those who only start getting acquainted with the world of PostgreSQL. It contains some basic information about this DBMS and its main features, history of its creation and development roadmap, step-by-step installation instructions and a getting started guide. ...

This book covers both classical and modern models in deep learning. The chapters of this book span three categories:
The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec.
Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines.
Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks ...

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python.
The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains.
After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. ...

Leverage various CSS features in combination with popular architectures in order to bring your style sheets back under your control. While CSS is the primary technology used for building beautiful web user interfaces, the style sheet files themselves are often quite ugly; left chaotic and unstructured through lack of a consistent architectural approach. By addressing the structure of your style sheets in the same way that you do with code, see how it is possible to create style rules that are clean and easy to read. Dig deep into CSS fundamentals and learn how to use the available selectors to build powerful rules.
You will learn how to use cascading, inheritance, pseudo-classes, pre-processors, and components to produce cleaner, DRY-er style sheets, and how to let these features work for you instead of leading you down the road of rule duplication and design inconsistencies. Embrace the clean, semantic HTML to make your code easier to read, while supporting accessibilit ...

Effectively manage Apple devices anywhere from a handful of Macs at one location to thousands of iPhones across many locations. This book is a comprehensive guide for supporting Mac and iOS devices in organizations of all sizes.
You'll learn how to control a fleet of macOS clients using tools like Profile Manager, Apple Device Enrollment Program (DEP), and Apple Remote Desktop. Then integrate your Mac clients into your existing Microsoft solutions for file sharing, print sharing, Exchange, and Active Directory authentication without having to deploy additional Mac-specific middle-ware or syncing between multiple directory services.
Apple macOS and iOS System Administration shows how to automate the software installation and upgrade process using the open source Munki platform and provides a scripted out-of-the box experience for large scale deployments of macOS endpoints in any organization. Finally, you'll see how to provision and manage thousands of iOS ...